Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this application into standard diabetic care protocols requires to overcome technical issues, particularly the foot sole segmentation. In this work we implemented and evaluated several segmentation approaches which include conventional and Deep Learning methods. Multimodal images, constituted by registered visual-light, infrared and depth images, were acquired for 37 healthy subjects. The segmentation methods explored were based on both visual-light as well as infrared images, and optimization was achieved using the spatial information provided by the depth images. Furthermore, a ground truth was established from the manual segmentation performed by two independent researchers. Overall, the performance level of all the implemented approaches was satisfactory. Although the best performance, in terms of spatial overlap, accuracy, and precision, was found for the Skin and U-Net approaches optimized by the spatial information. However, the robustness of the U-Net approach is preferred.To investigate whether finite element (FE) analysis of the spine in routine thoracic/abdominal multi-detector computed tomography (MDCT) can predict incidental osteoporotic fractures at vertebral-specific level; Baseline routine thoracic/abdominal MDCT scans of 16 subjects (8(m), mean age 66.1 ± 8.2 years and 8(f), mean age 64.3 ± 9.5 years) who sustained incidental osteoporotic vertebral fractures as confirmed in follow-up MDCTs were included in the current study. Thoracic and lumbar vertebrae (T5-L5) were automatically segmented, and bone mineral density (BMD), finite element (FE)-based failure-load, and failure-displacement were determined. These values of individual vertebrae were normalized globally (g), by dividing the absolute value with the average of L1-3 and locally by dividing the absolute value with the average of T5-12 and L1-5 for thoracic and lumbar vertebrae, respectively. Mean-BMD of L1-3 was determined as reference. Receiver operating characteristics (ROC) and area under the curve (AUC) were calculated for different normalized FE (Kload, Kdisplacement,K(load)g, and K(displacement)g) and BMD (KBMD, and K(BMD)g) ratio parameter combinations for identifying incidental fractures. Kload, K(load)g, KBMD, and K(BMD)g showed significantly higher discriminative power compared to standard mean BMD of L1-3 (BMDStandard) (AUC = 0.67 for Kload; 0.64 for K(load)g; 0.64 for KBMD; 0.61 for K(BMD)g vs. 0.54 for BMDStandard). The combination of Kload, Kdisplacement, and KBMD increased the AUC further up to 0.77 (p less then 0.001). The combination of FE with BMD measurements derived from routine thoracic/abdominal MDCT allowed an improved prediction of incidental fractures at vertebral-specific level.In this study, cellulose-based derivatives with heterocyclic moieties were synthesized by reacting cellulose with furan-2-carbonyl chloride (Cell-F) and pyridine-2,6-dicarbonyl dichloride (Cell-P). The derivatives were evaluated as adsorbents for the pesticide tetraconazole from aqueous solution. The prepared adsorbents were characterized by SEM, TGA, IR, and H1 NMR instruments. https://www.selleckchem.com/products/procyanidin-c1.html To maximize the adsorption efficiency of tetraconazole, the optimum conditions of contact time, pH, temperature, adsorbent dose, and initial concentration of adsorbate were determined. The highest removal percentage of tetraconazole from water was 98.51% and 95% using Cell-F and Cell-P, respectively. Underivatized nanocellulose was also evaluated as an adsorbent for tetraconazole for comparison purpose, and it showed a removal efficiency of about 91.73%. The best equilibrium adsorption isotherm model of each process was investigated based on the experimental and calculated R2 values of Freundlich and Langmuir models. The adsorption kinetics were also investigated using pseudo-first-order, pseudo-second-order, and intra-particle-diffusion adsorption kinetic models. The Van't Hoff plot was also studied for each adsorption to determine the changes in adsorption enthalpy (?H), Gibbs free energy (?G), and entropy (?S). The obtained results showed that adsorption by Cell-F and Cell-P follow the Langmuir adsorption isotherm and the mechanism follows the pseudo-second-order kinetic adsorption model. The obtained negative values of the thermodynamic parameter ?G (-4.693, -4.792, -5.549 kJ) for nanocellulose, Cell-F, and Cell-P, respectively, indicate a spontaneous adsorption process. Cell-F and Cell-P could be promising absorbents on a commercial scale for tetraconazole and other pesticides.The tumor suppressor protein p53 is frequently inactivated in human malignancies, in which it is associated with cancer aggressiveness and metastasis. Because p53 is heavily involved in epithelial-mesenchymal transition (EMT), a primary step in cell migration, p53 regulation is important for preventing cancer metastasis. p53 function can be modulated by diverse post-translational modifications including neddylation, a reversible process that conjugates NEDD8 to target proteins and inhibits the transcriptional activity of p53. However, the role of p53 in cancer migration by neddylation has not been fully elucidated. In this study, we reported that neddylation blockade induces cell migration depending on p53 status, specifically via the EMT-promoting transcription factor Slug. In cancer cell lines expressing wild type p53, neddylation blockade increased the transcriptional activity of p53 and expression of its downstream genes p21 and MDM2, eventually promoting proteasomal degradation of Slug. In the absence of p53, neddylation blockade increased cell migration by activating the PI3K/Akt/mTOR/Slug signaling axis.